基于改进鸡群算法的柔性作业车间调度问题求解

doi:10.16182/j.issn1004731x.joss.201707014

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (7): 1497-1505.doi: 10.16182/j.issn1004731x.joss.201707014

基于改进鸡群算法的柔性作业车间调度问题求解

许世鹏¹, 吴定会^1,2, 孔飞¹, 纪志成¹

1. 江南大学轻工过程先进控制教育部重点实验室,江苏无锡 214122;
2. 江苏省食品先进制造装备技术重点实验室,江苏无锡 214122

收稿日期:2015-08-17 发布日期:2020-06-01
作者简介:许世鹏(1992-),男,河南平顶山,硕士,研究方向为车间优化调度;吴定会(1970-),男,安徽合肥,博士,副教授,硕导,研究方向为车间智能优化调度算法、物联网技术。
基金资助:
国家自然科学基金(61572237, 61573167),江苏省“六大人才高峰”(WLW-008)

Solving Flexible Job-Shop Scheduling Problem by Improved Chicken Swarm Optimization Algorithm

Xu Shipeng¹, Wu Dinghui^1,2, Kong Fei¹, Ji Zhicheng¹

1. Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, China;
2. Key Laboratory of Advanced Manufacturing Equipment Technology of Food in Jiangsu Province, Wuxi 214122, China

Received:2015-08-17 Published:2020-06-01

摘要/Abstract

摘要： 为求解柔性作业车间调度问题,提出一种改进鸡群算法。以机器的最大完工时间为优化目标建立了柔性作业车间调度模型。提出改进鸡群算法,算法对小鸡的更新公式进行改进,并融合模拟退火算法和动态余弦惯性权重策略的优势,实现了全局搜索和局部探索的有效平衡。对4个标准函数和一个柔性作业车间调度模型进行仿真测试,与标准粒子群算法和鸡群算法相比,最大完工时间的最优值分别减少了12和7,平均值分别减少了16.3和5.7,验证了所提算法的有效性和优越性。

关键词: 柔性作业车间调度, 改进鸡群算法, 模拟退火算法, 动态余弦

Abstract: To solve the flexible job-shop scheduling problem (FJSP) more effectively, an improved chicken swarm optimization (ICSO) algorithm was proposed. A flexible job-shop scheduling model was established for the purpose of minimizing the machine makespan. The improved chicken swarm optimization algorithm was presented. Algorithm improved the update formula of chicks and combined the advantages of simulated annealing algorithm and dynamic inertia cosine weight strategy, which achieved an effective balance of global search and local exploration. According to simulating and testing four standard functions and a flexible job shop scheduling model and compared with particle swarm optimization (PSO) and chicken swarm optimization (CSO), makespan of the optimal value of ICSO is reduced by 12 and 7 respectively, and the mean value is reduced by 16.3 and 5.7, validating the effectiveness and the superiority of ICSO.

Key words: flexible job-shop scheduling, improved chicken swarm optimization algorithm, simulated annealing algorithm, dynamic inertia cosine

中图分类号:

TP391.9

许世鹏, 吴定会, 孔飞, 纪志成. 基于改进鸡群算法的柔性作业车间调度问题求解[J]. 系统仿真学报, 2017, 29(7): 1497-1505.

Xu Shipeng, Wu Dinghui, Kong Fei, Ji Zhicheng. Solving Flexible Job-Shop Scheduling Problem by Improved Chicken Swarm Optimization Algorithm[J]. Journal of System Simulation, 2017, 29(7): 1497-1505.

参考文献

[1] Jia S, Hu Z H.Path-relinking Tabu search for the multi-objective flexible job shop scheduling problem[J]. Computers & Operations Research (S0305-0548), 2014, 47(9): 11-26.
[2] Brucker P, Schlie R.Job-shop scheduling with multi-purpose machines[J]. Computing (S0010-485X), 1990, 45(4): 369-375.
[3] Driss I, Mouss K N, Laggoun A.A new genetic algorithm for flexible job-shop scheduling problems[J]. Journal of Mechanical Science and Technology (S1738-494X), 2015, 29(3): 1273-1281.
[4] Thammano A, Phu-ang A. A Hybrid Artificial Bee Colony Algorithm with Local Search for Flexible Job-Shop Scheduling Problem[J]. Procedia Computer Science (S1877-0509), 2013, 20(9): 96-101.
[5] Lalwani S, Kumar R, Gupta N.A Review on Particle Swarm Optimization variants and their applications to Multiple Sequence Alignment[J]. Journal of Applied Mathematics and Bioinformatics (S1792-6602), 2013, 3(2): 87-124.
[6] Dorigo M, Caro G, Gambardella L.Ant algorithms for discrete optimization[J]. Artificial life (S1064-5462), 1999, 5(2): 137-172.
[7] 李修琳, 鲁建厦, 柴国钟, 等. 混合蜂群算法求解柔性作业车间调度问题[J]. 计算机集成制造系统, 2011, 17(7): 1495-1500.
(Li X L, Lu J S, Chai G Z, et al.Hybrid Bee Colony Algorithm for Solving Flexible Job Shop Scheduling Problem[J]. Computer Integrated Manufacturing Systems, 2011, 17(7): 1495-1500.)
[8] 陈成, 邢立宁. 求解柔性作业车间调度问题的遗传-蚁群算法[J]. 计算机集成制造系统, 2011, 17(3): 615-621.
(Chen C, Xing L N.Genetic Algorithm - Ant Colony Algorithm for Solving Flexible Job Shop Scheduling Problem[J]. Computer Integrated Manufacturing Systems, 2011, 17(3): 615-621.)
[9] Karthikeyan S, Asokan P, Nickolas S.A hybrid discrete firefly algorithm for multi-objective flexible job shop scheduling problem with limited resource constraints[J]. The International Journal of Advanced Manufacturing Technology (S0268-3768), 2014, 72(9/12): 1567-1579.
[10] Meng X, Liu Y, Gao X, et al.A New Bio-inspired Algorithm: Chicken Swarm Optimization [M]// Advances in Swarm Intelligence. Germany: Springer International Publishing, 2014: 86-94.
[11] Ho N B, Tay J C, Lai E M K. An effective architecture for learning and evolving flexible job-shop schedules[J]. European Journal of Operational Research (S0377-2217), 2007, 179(2): 316-333.
[12] 焦晓璇, 景博, 黄以锋, 等. 基于模拟退火离散粒子群算法的测试点优化[J]. 计算机应用, 2014, 34(6): 1649-1652.
(Jiao X X, Jing B, Huang Y F, et al.Optimization for test selection based on simulated annealing binary particle swarm optimization algorithm[J]. Journal of Computer Applications, 2014, 34(6): 1649-1652.)
[13] 陈贵敏, 贾建援, 韩琪. 粒子群优化算法的惯性权值递减策略研究[J]. 西安交通大学学报, 2006, 40(1): 53-56.
(Chen G M, Jia J Y, Han Q.Study on the Strategy of Decreasing Inertia Weight in Particle Swarm Optimization Algorithm[J]. Journal of Xi'an Jiaotong University, 2006, 40(1): 53-56.)
[14] 田旻. 基于Baldwin效应粒子群优化算法的研究 [D]. 西安: 西安电子科技大学, 2012.
(Tian M.Research on Particle Swarm Optimization Algorithm Based on Baldwin Effect [D]. Xi'an, China: Xi'an University of Electronic Science and Technology, 2012.)

基于改进鸡群算法的柔性作业车间调度问题求解

Solving Flexible Job-Shop Scheduling Problem by Improved Chicken Swarm Optimization Algorithm

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 12

编辑推荐

Metrics

本文评价

[1]	陈魁, 毕利, 王文雅. 柔性作业车间AGV与机器双资源集成调度研究[J]. 系统仿真学报, 2022, 34(3): 461-469.
[2]	陈魁, 毕利. 改进粒子群算法在考虑运输时间下的FJSP研究[J]. 系统仿真学报, 2021, 33(4): 845-853.
[3]	陆嘉波, 程培星, 黄羿, 姚进强, 杨雪梦, 马新强, 刘勇. 用于时敏目标打击链快速构建的智能方法[J]. 系统仿真学报, 2021, 33(2): 346-357.
[4]	刘炜, 许嘉轩, 王沛沛, 刘瑞龙, 唐靖坤. 基于模拟退火算法的列车节能操纵研究[J]. 系统仿真学报, 2018, 30(6): 2320-2327.
[5]	李珂, 王艳, 纪志成. 基于混合策略的入侵杂草算法的FJSP问题研究[J]. 系统仿真学报, 2018, 30(5): 1918-1926.
[6]	张新, 李珂, 严大虎, 纪志成. 改进入侵杂草算法求解柔性作业车间调度问题[J]. 系统仿真学报, 2018, 30(11): 4469-4476.
[7]	张晓星, 王艳, 严大虎, 纪志成. 改进的混合蛙跳算法求解柔性作业车间调度[J]. 系统仿真学报, 2017, 29(9): 2093-2099.
[8]	陈超, 王艳, 严大虎, 纪志成. 面向能耗的柔性作业车间动态调度研究[J]. 系统仿真学报, 2017, 29(9): 2168-2175.
[9]	戴月明, 王明慧, 王春, 王艳. 骨干双粒子群算法求解柔性作业车间调度问题[J]. 系统仿真学报, 2017, 29(6): 1268-1276.
[10]	蒋莹莹, 纪志成. 基于混合量子粒子群算法的DFIG参数辨识[J]. 系统仿真学报, 2016, 28(5): 1054-1062.
[11]	张国辉, 吴立辉, 聂黎, 王永成. 考虑机器故障的柔性作业车间鲁棒调度方法[J]. 系统仿真学报, 2016, 28(4): 867-873.
[12]	田娜, 纪志成. 改进量子粒子群求解多目标柔性作业车间调度[J]. 系统仿真学报, 2015, 27(12): 2948-2957.